DeepKeep Demonstrates Superior Multilingual AI Security Performance in New Benchmark Study

As the gap in the multilingual security of AI guardrails grows, new benchmark research reveals that DeepKeep's ability to detect prompt injection and PII across languages outperforms others, including Meta and Nvidia

TEL AVIV, Israel, July 22, 2026 /PRNewswire/ -- DeepKeep, the end-to-end AI security platform, today unveiled the results of a new benchmark study showcasing significant improvements in the performance and efficiency of its multilingual AI security solution. The research study compared DeepKeep's approach to other guardrails and LLM-as-a-Judge methods and demonstrated that DeepKeep's multilingual capability provides superior accuracy and consistency.

As organizations deploy AI tools across global teams, the prompts and interactions these systems process increasingly span multiple languages. However, many AI security systems today are designed primarily for English-language prompts, and the consequences are measurable. A Brown University study found that translating unsafe inputs into low-resource languages got GPT-4 to engage with harmful requests 79% of the time, versus under 1% in English. As enterprises deploy AI globally, attackers can exploit this gap by issuing malicious prompts in other languages to bypass guardrails. While translation-based security solutions exist, these often introduce latency, lose context, or produce inconsistent results across languages, creating a growing security blind spot for multinational enterprises.

DeepKeep directly analyzes the semantic meaning of prompts and responses across languages using a cognition-based analysis, allowing the system to classify data without translating it into English first. The cognition-based solution can deliver an interpretable response, allowing the system to learn and improve in time. Due to their design, DeepKeep's guardrails can efficiently handle zero-day attacks. The guardrails are also designed to handle mixed-language prompts seamlessly – a scenario increasingly common in enterprise environments where users combine multiple languages within a single AI query.

In benchmark testing against widely used open source models, including Meta's LLaMa Prompt Guard and Nvidia's NeMo, DeepKeep significantly outperformed in detecting prompt injection attempts and Personal Identifiable Information (PII).

The evaluation used several widely recognized prompt injection datasets, including SafeGuard, Wild Jailbreak, and Alpaca, with test sets translated into 12 additional languages including Japanese, German, Spanish, French, Italian, Korean, Dutch, and Portuguese. Across these benchmarks, DeepKeep achieved F1 scores approaching 0.98 in prompt injection detection while significantly reducing false negatives compared to translation-based guardrail models.

The platform maintains consistent security decisions across languages while operating with smaller, more efficient model architecture. DeepKeep's multilingual classifier operates with a model of roughly 400 million parameters, which is significantly smaller than many guardrail models that rely on multi-billion-parameter architectures, enabling faster inference and lower latency in enterprise deployments.

"AI security has largely been built around the assumption that prompts are written in English or that translation is sufficient. That assumption no longer reflects how enterprises actually use AI," said Yossi Altevet, CTO and Co-Founder at DeepKeep. "As AI systems are deployed across global teams and markets, security models must understand intent across languages, not just words. That shift requires a fundamentally different approach to how AI interactions are analyzed and protected, and we're proud to have created a solution that meets this challenge with flying colors."

DeepKeep's multilingual security solution is part of the company's established suite of enterprise AI security solutions that give businesses the confidence to leverage AI without sacrificing safety, control, or trust.

For more information about DeepKeep's multilingual AI security research, read the full analysis here.

About DeepKeep

DeepKeep provides end-to-end AI security and trustworthiness across the full AI lifecycle. Its platform protects multimodal systems – including large language models and computer vision – helping enterprises deploy and use AI safely, accurately, and in compliance with security and privacy standards. With capabilities such as an AI Firewall, Vibe and Automated AI Red Teaming, AI Usage Control and advanced Model Scanning, DeepKeep enables cybersecurity teams to defend against vulnerabilities, data leakage, hallucinations, and bias while maintaining trust in AI-driven operations. Founded in 2021, DeepKeep is dedicated to securing the future of enterprise AI. For more information, visit www.deepkeep.ai.

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SOURCE DeepKeep